activity
20242026
collaborators

9 papers

physics.bio-ph2026

A stochastic agent-based extension of the GSM2 model for particle therapy: cell-cycle dynamics, dose-rate dependence, and fractionation effects

Francesco G. Cordoni, Marco Battestini, Marta Missiaggia

Accurately linking microscopic energy deposition from ionizing radiation to emergent biological outcomes remains a central challenge in radiobiological modelling, particularly when…

physics.ins-det2026

Measurements of the micro-spill structure of medical cyclotron and synchrotron beams and its impact on pulse pileup

Matthias Knopf, Simon Waid, Stefan Gundacker +10

Detector characterization and instrumentation testing are often performed at cyclotron and synchrotron facilities, many of which were originally developed for medical applications…

physics.med-ph2026

Mechanistic driven TCP and NTCP modeling for particle therapy accounting for a broad range of physical irradiation parameters and tissue environmental conditions

Marco Battestini, Jules Morand, Giulio Bordieri +3

In conventional radiotherapy, the probability of controlling tumor growth is quantified using Tumor Control Probability (TCP) models. Instead, the probability of experiencing a sid…

physics.bio-ph2026

One scale to rule them all: interpretable multi-scale Deep Learning for predicting cell survival after proton and carbon ion irradiation

Giulio Bordieri, Giorgio Cartechini, Anna Bianchi +4

The relationship between the physical characteristics of the radiation field and biological damage is central to both radiotherapy and radioprotection, yet the link between spatial…

physics.med-ph2026

A combined dose and microdosimetric modeling framework incorporating volume effects correlates with tissue sparing in proton minibeam radiotherapy

Giulio Bordieri, Marco Battestini, Gianluca Lattanzi +4

Proton minibeam (pMB) radiotherapy, delivers highly heterogeneous dose distributions alternating high-dose peaks and low-dose valleys. This aims to widen the therapeutic window by…

physics.med-ph2025

Machine Learning-based beam delivery time model for Mevion 250i with Hyperscan technology

Giorgio Cartechini, Francesco Giuseppe Cordoni, Mirko Unipan +1

Purpose: Accurate prediction of beam delivery time (BDT) is essential for operational efficiency, 4D dose calculations, and advanced proton therapy techniques. Despite its importan…